Mapping the 2017-18 Pa. flu season

This project is the final assignment for the course “Introduction to R for Journalists” taught by Andrew Ba Tran.

We’re looking at the 2017-28 flu season in Pennsylvania by county.

Bring in the data

I used a table capture browser extension to grab this data from the Pennsylvania Department of Health

# Import in CSV with total flu cases, create new DF with only total 
flu_18<- read_csv("~/Documents/Census_stuff/Pa_flu_cases/PA_final_flu_18.csv") 
# Filter down to only total cases and 
flu_18<- select(flu_18, County, Total_flu_cases) 
datatable(flu_18,extensions = 'Buttons', options = list(
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))

Get county population figures via CensusAPI

Since we are trying to look at the flu’s impact on a county-by-county basis, we need to use the CensusAPI package to bring in the popuation stats.

# Looking at the various Apis available 
pop_var <- listCensusMetadata(name= "pep/population", 
                              vintage = 2017,
                              type= "variables")

pop_vars <- listCensusMetadata(name = "pep/population", vintage = 2017, 
                                 type = "geography")
#Getting the Census population data
pa_pop<- getCensus("pep/population",
          vintage=2017,
          vars=c("POP", "GEONAME"),
          region="county:*",
          regionin = "state:42")

Cleaning data

We need to separate the county name from the state name because the flu data is by county only.

pa_pop_2 <- separate(pa_pop,col = GEONAME, "County", " County, Pennsylvania") %>% 
  select(County,POP) 

pa_pop_2$POP <-as.numeric(pa_pop_2$POP)

Joining the data

# Joining the total flu cases with the population data
pa_flu_rate <-full_join(flu_18,pa_pop_2, by="County")

# Adjusting for a flu rate per 100,000 residents
pa_flu_rate$per_capita <- round(pa_flu_rate$Total_flu_cases/pa_flu_rate$POP*100000,0)

Charting the data

When I create these maps for work, we are interested in highlighting the top 15-25 counties that are being hardest hit by the flu. Here’s a look at that in a scatter plot.

Now we need to bring in the county shapefiles to make the map.

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Creating the map

library (leaflet)

# Joining the flu rate info with the shapefile
pa_flu_rate_merged <- geo_join(counties,pa_flu_rate,"NAME","County")

#Creating a color palette
pal<- colorNumeric("Blues", domain= pa_flu_rate_merged$per_capita)

pa_flu_rate_merged <- subset(pa_flu_rate_merged, !is.na(Total_flu_cases))

#Creating a popup for the map
popup_flu <-paste0("County: ", as.character(pa_flu_rate_merged$County),
"<br> Flu rate: ", as.numeric(pa_flu_rate_merged$per_capita), "<br> Total cases: ", 
as.numeric(pa_flu_rate_merged$Total_flu_cases))

# starting to build the map
counties %>% 
  leaflet() %>% 
addTiles() %>% 
  addPolygons(popup = ~popup_flu)
# Mapping it with the new tiles CartoDB.Positron
leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  setView(-80.000402, 40.441153, zoom = 6) %>% 
  addPolygons(data = pa_flu_rate_merged , 
              fillColor = ~pal(pa_flu_rate$per_capita), 
              fillOpacity = 0.7, 
              weight = 0.2, 
              smoothFactor = 0.2, 
              popup = ~popup_flu) %>%
  addLegend(pal = pal, 
            values = pa_flu_rate_merged$per_capita, 
            position = "bottomright", 
            title = "Flu rate per 100,000 residents")